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A Look at Choropleth Maps

Fresh new post from Severino Ribecca, in his special series about the main types of charts

June 30, 2015

[This is a guest post by Severino Ribecca*, as part of a series dedicated to each individual kind of chart that he has read into as part of his main research project.]

 

 

Maps are a useful and important tool to navigate a geographical region and a way to communicate information related to it. Information displayed on a map doesn’t necessarily have to relate directly to its geography, additional, abstract information can also be applied.

During the 19th century, thematic maps became popular and were used as a tool to both demonstrate and persuade. Well known for his use of maps to promote political causes was Baron Pierre Charles Dupin, a 19th century French mathematician, engineer and economist. Dupin believed there was a close relationship between a person’s education level and their prosperity. After Dupin had collected research on the levels of illiteracy across France, he needed a way to communicate how this problem was distributed over his homeland.

Inspired by the work of the German statisticians Georg Hassel and August Friedrich Wilhelm Crome, Dupin developed his first choropleth map “Carte figurative de l’instruction populaire de la France” (distribution and intensity of illiteracy in France).

(iSource: York University – (Left) Dupin portrait with map on desk, (Right) Carte figurative de l’instruction populaire de la France)

Dupin’s geographical solution was to shade the different regions of France from light to dark. His choice of colour used (black to white) was intentional:

…the gradual shadings of the map were directly inspired by a metaphoric conception of knowledge. The shading gave the impression of a light thrown on the map, comparable to the light of knowledge. Moreover, Dupin often used the expression “dark” and “enlightened France”… (Palsky 5)

Light was used as a symbol for “enlightenment” and therefore was used to represent the regions of France that were more literate. The logic behind this metaphor was that light allows you to see the world around you, while the darkness leaves you in a state of ignorance and obscurity. From looking at Dupin’s map, you can see a contrast between north and south France, which suggests an educational disparity between both parts of the country.

While Dupin created the first instance of a “Choropleth” map, the name wasn’t introduced until 1938 by the geographer John Kirkland Wright. It’s likely due to his interest in Ancient Greek history, that Wright combined the Greek words χώρο (pronounced chó̱ro) meaning “area or region” and πλήθος (plí̱thos) meaning multitude to give the map it’s name.

Using Choropleth Maps

As shown with Dupin’s map, Choropleths display geographical data by colouring regions on a map in relation to its data. This visualises values over a map, which can reveal variations or patterns across it.

USA_choropleth

The colouring system used in Choropleth maps depends on when whether you’re visualising numerical values (like above) or categorical data. A legend needs need be provided alongside the map in order for readers to decode the values or categories displayed.

When visualising numerical values, usually in the form of statistics, then a progressive colouring system is required. This means that colour shade varies as values in the data increases. The colour scale displayed in the chart legend can either be on a continuous scale, or ideally, divided into range classes: 0-10, 11-20, 21-30 and so on. In principle, lighter colours should represent the lower end of the value scale, while the darker colours represent the higher values.

There are different types of colour progression that can be used on a Choropleth map:

Monochromatic: A typical example of this would be black to white, like Dupin’s map, but any single colour can be used (so red fading into white for example).
Bi-polar: uses two different colours at opposites end of the value scale that either blend into each other or fade into white in the middle. Typically, both colours are usually opposite hues or complementary colours.
Bi-variate: a colour system that accompanies two variables by assigning a colour to each of them, then combining the two via a matrix of blended shades. Read more on this method here.
Multi-colour: here the colour progression uses a number of significantly different hues, so that each range on the legend is a different colour.

When displaying the geographical distribution of categorical data on Choropleth map, then the colour system is based on giving each category a colour. This is a popular method for displaying political influence across a country, where each political party is assigned a colour.

choropleth2

While using colour to represent values is great for giving a quick, generalised view of the data, you can’t accurately read or compare values from looking at the map. Another flaw with Choropleth maps is that larger regions appear to be emphasised more than smaller ones, distorting the reader’s perceptions of values on the map.

Something you need to remember when working with Choropleth maps is that the data often needs to be normalised in order for the it to be accurately represented. So if you’re displaying raw data based on a population within a region, then the data needs to be divided per square mile/km.

Contemporary Choropleths

Below are some recent applications of Choropleth maps for a range of different topics.

UK Peace Index - visionofhumanity.org
UK Peace Index – visionofhumanity.org

UK Peace Index – visionofhumanity.org

This interactive Choropleth map displays the levels of peace across the UK according to five key indicators: homicide, violent crime, weapons crime, public disorder and police officer numbers. There’s also an option to see the data change over time (2003 to 2012). From this is you can see that the UK has become significantly more peaceful over the ten year period.

Prostitution, pimping and brothels: how legal are they across the world? - telegraph.co.uk
Prostitution, pimping and brothels: how legal are they across the world? – telegraph.co.uk

Prostitution, pimping and brothels: how legal are they across the world? – telegraph.co.uk

Another interactive map that displays worldwide the status of prostitution in four distinctly coloured
categories: fully legal, restricted, Swedish model and totally illegal. Tooltips provide extra information on each individual country by displaying the legality of brothels, pimping and general prostitution.

Reviving the Statistical Atlas of the United States with New Data - flowingdata.com
Reviving the Statistical Atlas of the United States with New Data – flowingdata.com

Reviving the Statistical Atlas of the United States with New Data – flowingdata.com

Nathan Yau produced his own update of the Statistical Atlas of the United States in a nostalgic series of maps and charts that visualise statistical data on a wide range of subjects like geology, weather, demographics, education, economics and transportation. In the example above, Yau has visualised the geographic distribution of various ancestries across the United States.

In the next post, I will be looking at Pie & Donut Charts.

Research and further reading:

Choropleth Map Reference Page – The Data Visualisation Catalogue
Connections and exchanges in European thematic cartography – By Gilles Palsky
Take Care of your Choropleth Maps – vis4.net
The Choropleth Map Presentation Slides – Hunter College, Depart of Geography
Choropleth Map – Seeing Data
Learn more Choropleth maps – indiemapper
Concept Gallery – e-education.psu.edu

 

*Severino Ribecca is a British graphic and information designer interested in data visualization. Currently he’s building an online library of different information visualization methods called The Data Visualisation Catalogue. You can follow the project’s updates on Twitter (@dataviz_catalog) and support further developments on the Patreon Page.

Written by Tiago Veloso

Tiago Veloso is the founder and editor of Visualoop and Visualoop Brasil . He is Portuguese, currently based in Bonito, Brazil.

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  • http://www.joshuastevens.net/ Josh

    Very nice overview! The historical context and outline of use cases is nice.

    One thing I would add though is a minor correction to this: “While using colour to represent values is great for giving a quick, generalised view of the data, you can’t accurately read or compare values from looking at the map.”

    This is only true for *unclassed* choropleth maps (which in general, are a data viz no-no…for exactly the reason you state). Using appropriate class breaks however makes choropleth maps function incredibly well: any unit on the map can be paired to the exact class in the legend, and comparisons are then very effective.

    It would be worth noting that unclassed vs classed is another color progression choice that can be used with choropleth maps, though only the latter should be recommended.

    For more information on data classification and how users interpret them, see this article (PDF) from Cindy Brewer and Linda Pickle: http://www.personal.psu.edu/cab38/Pub_scans/Brewer-Pickle_2002_Epi-Choropleth-Classing_Annals.pdf

  • Severino R

    Thanks for linking to that paper, I’ll have to have a read through. You’ve made a good point there that I’ve missed out in terms of the classed choropleths being a much better choice and more accurate.

    But I think even with classes you can’t read and compare exact numerical values, as the each class displays a range.

    Then again, if you’re colour-coding based on categories, then technically you’re reading the exact “values” there! I think I’ll have to mentioned these points next time.

  • http://www.joshuastevens.net/ Joshua Stevens

    “But I think even with classes you can’t read and compare exact numerical values, as the each class displays a range.”

    That’s absolutely correct, but the same is true for all data visualizations. It is the very purpose of visualization—to provide an overview of the data and reveal trends, patterns, and other structure that can be observed visually. The moment we turn quantities into simplified abstractions, raw values are replaced by color, size, or other visual variables.

    That’s the major tenet of Shneiderman’s Visual Information Seeking Mantra: “overview first, zoom and filter, then details on demand.” http://www.infovis-wiki.net/index.php/Visual_Information-Seeking_Mantra

    Simplified abstractions—such as choropleth maps—provide overviews of the data as a whole, while interactive techniques can be used to provide the raw quantities as the user requests them (via hover, clicking, or other techniques).

  • NavyDish

    Really nice article! I recently came across a great and simple online tool- Viz to create choropleth maps for free. viz.socialcops.com